- Open Access
Analysis of discrete modern Hopfield networks in open quantum system
Phys. Rev. Research 7, 033159 – Published 14 August, 2025
DOI: https://doi.org/10.1103/3966-8xs2
Abstract
The modern Hopfield network, proposed by Krotov and Hopfield, is a mathematical generalization of the Hopfield network, which is a basic model of associative memory that employs higher-order interactions. This study introduces an open quantum model for discrete modern Hopfield networks that generalizes the open quantum Hopfield network. Our model integrates dissipative quantum spin systems, governed by quantum master equations, with classical hopping terms and additional quantum effects through a transverse field. We analytically examined the behavior of the stable fixed points and numerically determined the phase diagram. The results demonstrated qualitatively distinct behaviors from the open quantum Hopfield network, showing that the ferromagnetic and limit cycle phases have additional stable fixed points.
Physics Subject Headings (PhySH)
Article Text
References (28)
- J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proc. Natl. Acad. Sci. USA 79, 2554 (1982).
- D. J. Amit, H. Gutfreund, and H. Sompolinsky, Spin-glass models of neural networks, Phys. Rev. A 32, 1007 (1985).
- D. J. Amit, H. Gutfreund, and H. Sompolinsky, Storing infinite numbers of patterns in a spin-glass model of neural networks, Phys. Rev. Lett. 55, 1530 (1985).
- D. H. Ackley, G. E. Hinton, and T. J. Sejnowski, A learning algorithm for Boltzmann machines, Cogn. Sci. 9, 147 (1985).
- E. Gardner, The space of interactions in neural network models, J. Phys. A: Math. Gen. 21, 257 (1988).
- H. Huang, Statistical Mechanics of Neural Networks (Springer, Singapore, 2021).
- D. Krotov and J. J. Hopfield, Dense associative memory for pattern recognition, Adv. Neural Inf. Process. Syst. 29, 1172 (2016).
- M. Demircigil, J. Heusel, M. Löwe, S. Upgang, and F. Vermet, On a model of associative memory with huge storage capacity, J. Stat. Phys. 168, 288 (2017).
- H. Ramsauer, B. Schäfl, J. Lehner, P. Seidl, M. Widrich, T. Adler, L. Gruber, M. Holzleitner, M. Pavlović, G. K. Sandve et al., Hopfield networks is all you need, arXiv:2008.02217.
- C. Lucibello and M. Mézard, Exponential capacity of dense associative memories, Phys. Rev. Lett. 132, 077301 (2024).
- D. Krotov and J. Hopfield, Large associative memory problem in neurobiology and machine learning, arXiv:2008.06996.
- B. Hoover, H. Strobelt, D. Krotov, J. Hoffman, Z. Kira, and D. H. Chau, Memory in plain sight: A survey of the uncanny resemblances between diffusion models and associative memories, arXiv:2309.16750.
- L. Ambrogioni, In search of dispersed memories: Generative diffusion models are associative memory networks, Entropy 26, 381 (2024).
- J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, Quantum machine learning, Nature (London) 549, 195 (2017).
- M. Schuld, I. Sinayskiy, and F. Petruccione, The quest for a quantum neural network, Quantum Info. Proc. 13, 2567 (2014).
- K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii, Quantum circuit learning, Phys. Rev. A 98, 032309 (2018).
- E. Farhi and H. Neven, Classification with quantum neural networks on near term processors, arXiv:1802.06002.
- M. Schuld and N. Killoran, Quantum machine learning in feature Hilbert spaces, Phys. Rev. Lett. 122, 040504 (2019).
- P. Rebentrost, T. R. Bromley, C. Weedbrook, and S. Lloyd, Quantum Hopfield neural network, Phys. Rev. A 98, 042308 (2018).
- N. Meinhardt, N. M. Neumann, and F. Phillipson, Quantum Hopfield neural networks: A new approach and its storage capacity, in Computational Science–ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3–5, 2020, Proceedings, Part VI 20 (Springer, Cham, 2020), pp. 576–590.
- P. Rotondo, M. Marcuzzi, J. P. Garrahan, I. Lesanovsky, and M. Müller, Open quantum generalisation of Hopfield neural networks, J. Phys. A: Math. Theor. 51, 115301 (2018).
- E. Fiorelli, P. Rotondo, M. Marcuzzi, J. P. Garrahan, and I. Lesanovsky, Quantum accelerated approach to the thermal state of classical all-to-all connected spin systems with applications to pattern retrieval in the Hopfield neural network, Phys. Rev. A 99, 032126 (2019).
- E. Fiorelli, I. Lesanovsky, and M. Müller, Phase diagram of quantum generalized Potts-Hopfield neural networks, New J. Phys. 24, 033012 (2022).
- E. Fiorelli, M. Müller, I. Lesanovsky, and F. Carollo, Mean-field dynamics of open quantum systems with collective operator-valued rates: validity and application, New J. Phys. 25, 083010 (2023).
- L. Bödeker, E. Fiorelli, and M. Müller, Optimal storage capacity of quantum Hopfield neural networks, Phys. Rev. Res. 5, 023074 (2023).
- S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, Optimization by simulated annealing, Science 220, 671 (1983).
- T. Kadowaki and H. Nishimori, Quantum annealing in the transverse Ising model, Phys. Rev. E 58, 5355 (1998).
- J. A. Hertz, R. G. Palmer, and A. S. Krogh, Introduction to the Theory of Neural Computation, 1st ed. (CRC Press, Boca Raton, 1991).